As input, Seurat takes single-cell RNA-seq data (1, left) from dissociated cells (e.g., cells A–C), where information about the original spatial context was lost during dissociation, and (2, right) in situ hybridization patterns for a series of landmark genes. To generate a binary spatial reference map, the tissue of interest is divided into a discrete set of user-defined bins, and the in situ data is binarized to reflect the detection of gene expression within each bin, as is shown for genes X, Y, and Z. (3) Seurat uses expression measurements across many correlated genes to ameliorate stochastic noise in individual measurements for landmark genes. As schematized, Seurat learns a model of gene expression for each of the landmark genes based on other variable genes in the dataset, reducing the reliance on a single measurement, and mitigating the effect of technical errors. Seurat then builds statistical models of gene expression in each bin (4) by relating the bimodal expression patterns of the RNA-seq estimates to the binarized in situ data. Shown are probability distributions for genes X, Y, and Z for three different embryonic bins. Finally, Seurat uses these models to infer the cell’s original spatial location (5), assigning posterior probability of origin (depicted in shades of purple) to each bin. Seurat can map exclusively to one bin (e.g., cell C), or assign probability to multiple bins in some cases (e.g., cells A & B).